Cusp Catastrophe Models for Cognitive Workload and Fatigue in Teams

The use of two cusp catastrophe models has been effective for untangling the effects of cognitive workload, fatigue, and other complications on the performance of individuals. This study is the first to use the two models to separate workload and fatigue effects on team performance. In an experiment involving an emergency response simulation, 360 undergraduates were organized into 44 teams. Workload was varied by team size, number of opponents, and time pressure. The cusp models for workload and fatigue were more accurate for describing trends in team performance criteria compared to linear alternatives. Individual differences in elasticity-rigidity were less important than subjective workload and experimental conditions as control variables. Fluid intelligence within the team was an important compensatory ability in the fatigue model. Results further supported the nonlinear paradigm for the assessment of cognitive workload and fatigue and demonstrated its effectiveness for understanding team phenomena

Individual Differences in the Experience of Cognitive Workload

This study investigated the roles of four psychosocial variables – anxiety, conscientiousness, emotional intelligence, and Protestant work ethic – on subjective ratings of cognitive workload as measured by the Task Load Index (TLX) and the further connections between the four variables and TLX ratings of task performance. The four variables represented aspects of an underlying construct of elasticity versus rigidity in response to workload. Participants were 141 undergraduates who performed a vigilance task under different speeded conditions while working on a jigsaw puzzle for 90 minutes. Regression analysis showed that anxiety and emotional intelligence were the two variables most proximally related to TLX ratings. TLX ratings contributed to the prediction of performance on the puzzle, but not the vigilance task. Severity error bias was evident in some of the ratings. Although working in pairs improved performance, it also resulted in higher ratings of temporal demand and perceived performance pressure

Theory and application of mathematical science

A sudden jump in the value of the state variable in a certain dynamical system can be studied through a catastrophe model. This paper presents an application of catastrophe model to solve a psychological problems. Since we will have three psychological aspects or parameters. Intelligence (I), Emotion (E), and Adversity (A), a Swallowtail catastrophe model is considered to be an appropriate one. Our methodology consists of three steps : solving the Swallowtail potential function, finding the critical points up to and including three-fold degenerates, and fitting the model into our measured data. Using a polynomial curve fitting derived from the potential function of Swallowtail Catastrophe Model, relations among three parameters combinations are analyzed. Results show that there are catastrophe phenomena for each relations, meaning that a small change in one psychological aspect may cause a dramatically change in another aspec

A catastrophe-theory model for simulating behavioral accidents

Behavioral accidents are a particular type of accident. They are caused by inappropriate individual behaviors and faulty reactions. Catastrophe theory is a means for mathematically modeling the dynamic processes that underlie behavioral accidents. Based on a comprehensive data base of mining accidents, a computerized catastrophe model has been developed by the Bureau of Mines. This model systematically links individual psychological, group behavioral, and mine environmental variables with other accident causing factors. It answers several longstanding questions about why some normally safe behaving persons may spontaneously engage in unsafe acts that have high risks of serious injury. Field tests with the model indicate that it has three important uses: It can be used as an effective training aid for increasing employee safety consciousness; it can be used as a management laboratory for testing decision alternatives and policies; and it can be used to help design the most effective work teams

Nonlinearity and Gestalt Therapy : Back to the Beginning

Gestalt methods are explored in the context of the nonlinear view. The original Lewinian equations are updated to the current notions of nonlinear dynamics and current Gestalt Therapy theory. Ordinary differential equations and nonlinear processes are applied some of the key features of Gestalt Theory. Theoretical positions are explored and therapeutic impact considered

Change is not always linear: The study of nonlinear and discontinuous patterns of change in psychotherapy.

Abstract The study of discontinuities and nonlinear change has been a fruitful endeavor across the sciences, as these shifts can provide a window into the organization of complex systems and the processes that are associated with transition. A common assumption in psychotherapy research has been that change is gradual and linear. The research designs and statistics used to study change often reflect this assumption, but some recent research reveals other patterns of change. We briefly review relevant literature on dynamical systems theory and on life transition and post-traumatic growth to highlight the significance of nonlinear and discontinuous change across areas of psychology. We describe recent applications of these ideas and methods to the study of change in psychotherapy and encourage their use to complement more traditional clinical trial designs. © 2007 Elsevier Ltd. All rights reserved. Some change can be gradual and incremental, but many systems in nature show periods of turbulence and instability, with dramatic changes or growth spurts. Ilya Prigogine, a Nobel laureate known for his theory of dissipative structures in chemistry, argues that instabilities play an important role in transformation and that "most of reality, instead of being orderly, stable, and equilibrial, is seething and bubbling with change, disorder, and process" (Prigogine & Stengers, 1984, p. xv). The study of discontinuities has been a fruitful endeavor across the sciences, as these shifts can provide a window into the organization of a system and the processes that are associated with transition. A common assumption in psychotherapy research is that change is gradual and linear. The research designs and statistics used to study change often reflect this assumption. The hypothesized predictors of change are measured once or twice and then compared between groups or correlated with symptom change at the end of treatment. Most research also focuses on group averages, with much less emphasis on the rich information available in individual time course Clinical Psychology Review 27 (2007) 715 -72

A network perspective on suicidal behavior: understanding suicidality as a complex system

Background: Suicidal behavior is the result of complex interactions between many different factors that change over time. A network perspective may improve our understanding of these complex dynamics. Within the network perspective, psychopathology is considered to be a consequence of symptoms that directly interact with one another in a network structure. To view suicidal behavior as the result of such a complex system is a good starting point to facilitate moving away from traditional linear thinking. Objective: To review the existing paradigms and theories and their application to suicidal behavior. Methods: In the first part of this paper, we introduce the relevant concepts within network analysis such as network density and centrality. Where possible, we refer to studies that have applied these concepts within the field of suicide prevention. In the second part, we move one step further, by understanding the network perspective as an initial step toward complex system theory. The latter is a branch of science that models interacting variables in order to understand the dynamics of complex systems, such as tipping points and hysteresis. Results: Few studies have applied network analysis to study suicidal behavior. The studies that do highlight the complexity of suicidality. Complexity science offers potential useful concepts such as alternative stable states and resilience to study psychopathology and suicidal behavior, as demonstrated within the field of depression. To date, one innovative study has applied concepts from complexity science to better understand suicidal behavior. Complexity science and its application to human behavior are in its infancy, and it requires more collaboration between complexity scientists and behavioral scientists. Conclusions: Clinicians and scientists are increasingly conceptualizing suicidal behavior as the result of the complex interaction between many different biological, social, and psychological risk and protective factors. Novel statistical techniques such as network analysis can help the field to better understand this complexity. The application of concepts from complexity science to the field of psychopathology and suicide research offers exciting and promising possibilities for our understanding and prevention of suicide